import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands

import json
import time
from websocket import create_connection

address="ws://127.0.0.1:10000"


class websocket:
    def __init__(self,address):
        self.ws = create_connection(address)
        
    def send(self,params):
        print("Sending ...")
        self.ws.send(params)

    def quit(self):
        self.ws.close() 

    def registry(self):
      registryData = {
                    "id": '',
                    "path": 'registry',
                    "query": {
                        "name": "mediapi"
                    },
                    "headers": {},
                    "body": '',
                }
      self.send(json.dumps(registryData))


webso=websocket(address)
webso.registry()



# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
    min_detection_confidence=0.8,
    min_tracking_confidence=0.8) as hands:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # Flip the image horizontally for a later selfie-view display, and convert
    # the BGR image to RGB.
    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    results = hands.process(image)
    if results.multi_hand_landmarks:
        
        for i,hand_landmarks in enumerate(results.multi_hand_landmarks):
            hlm = []
            for j,lm in enumerate(hand_landmarks.landmark):
             
              hlm.append({"x":lm.x,"y":lm.y,"z":lm.z,"v":lm.visibility,"index":j})
            postData = {
                "path":"/handData",
                "query":{"id":"mediapi"},
                "body":hlm,
              }
            strData = json.dumps(postData)
            print (strData)
            webso.send(strData)


        # for i,handedness in enumerate(results.multi_handedness):
            # hlm = []
            # hlm.append({"label":handedness.label,"score":handedness.score,"index":i})
            # print(dir(handedness))
        # print(results.multi_handedness[0].classification)



    # Draw the hand annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.multi_hand_landmarks:
      for hand_landmarks in results.multi_hand_landmarks:
        mp_drawing.draw_landmarks(
            image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
    # cv2.imshow('MediaPipe Hands', image)
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()